Cloud and edge integrated energy optimizer

ABSTRACT

An integrated energy optimizer having an edge side and a cloud side. The edge side may incorporate an energy optimizer, a building management system connected to the energy optimizer, a controller connected to the building management system, and equipment connected to the controller. The cloud side may have a cloud connected to the energy optimizer and to the building management system, and a user interface connected to the cloud. Data from the field sensor may go to the optimizer and the building management system. The data may be processed at the optimizer and the building management system for proper settings at the building management system.

This present Application is a divisional of U.S. patent application Ser.No. 16/417,545, filed May 20, 2019. U.S. patent application Ser. No.16/417,545, filed May 20, 2019, is hereby incorporated by reference.

BACKGROUND

The present disclosure pertains to optimization techniques andparticularly to optimization of energy usage relative to climate controlof a building.

SUMMARY

The disclosure reveals an integrated energy optimizer having an edgeside and a cloud side. The edge side may incorporate an energyoptimizer, a building management system connected to the energyoptimizer, a controller connected to the building management system, andequipment connected to the controller. The cloud side may have a cloudconnected to the energy optimizer and to the building management system,and a user interface connected to the cloud. Data from the field sensormay go to the optimizer and the building management system. The data maybe processed at the optimizer and the building management system forproper settings at the building management system.

BRIEF DESCRIPTION OF THE DRAWING

FIG. 1 is a diagram of an architecture for the present system;

FIG. 2 is a diagram of a solution architecture;

FIG. 3 is a diagram of a work flow of the present system blocknomenclature;

FIG. 4 is a diagram of an example implementation of edge optimization;and

FIG. 5 is a diagram of an example implementation of cloud analytics.

DESCRIPTION

The present system and approach may incorporate one or more processors,computers, controllers, user interfaces, wireless and/or wireconnections, and/or the like, in an implementation described and/orshown herein.

This description may provide one or more illustrative and specificexamples or ways of implementing the present system and approach. Theremay be numerous other examples or ways of implementing the system andapproach.

Aspects of the system or approach may be described in terms of symbolsin the drawing. Symbols may have virtually any shape (e.g., a block) andmay designate hardware, objects, components, activities, states, steps,procedures, and other items.

An issue addressed herein may be related to optimal heating, ventilationand air conditioning (HVAC) control. An aim of an optimizer type ofsystem is to minimize energy costs while maintaining a zone's comfortlevel regarding temperature, humidity, and so forth. A product based onit may be a setpoint optimizer that has been applied in many customersites and piloted on several company sites. The present system may firstpredict the energy demand of a building and then optimize the setpoints(actions) that can meet the demand with minimal cost based on currentmeasurements of HVAC plant variables.

For building solutions, the energy management setpoint optimizer maycollect plant data from a building management system (BMS) and send thedata to a cloud, where the optimizer is hosted and optimization isperformed. After the optimization results (optimal setpoints) aregenerated, they may be sent back to the enterprise buildings integratorand the controller setpoints may be changed accordingly.

The solution may have the following limitations. Firstly, there mayexist data transfer latency issue for large buildings, which is quitecommon for home and building system customers. It may take severalminutes to transfer several thousand point values from the enterprisebuildings integrator to a cloud, while the energy management setpointoptimizer may need real time (or near real time) data to understand thecurrent conditions.

Another limitation is that a setpoint change command may be from thecloud, which gives a customer concern not only for the time issue(although this kind of control is not so time critical as conventionalcontrol), but also for information technology (IT) security (e.g., manin the middle attack).

The noted issue may be addressed by splitting an original solutionstructure into two parts. One may be control and optimization on an edgedevice. Another may be analytics and model improvement on a cloud. Inthis way, an optimizer on edge side will not necessarily have a datatransfer and control latency issue, while the cloud side may improve themodel by leveraging big data analytics and pushing the updated model toan edge device.

The edge side may be noted. Real time data may be collected from a site,and energy optimization may be performed based on historical data,tariff information and a demand forecast periodically, e.g., in a 15minute step, and with a one hour optimization horizon. Optimizedsetpoints may be sent to site controllers and the controller setpointsmay be reset.

The cloud side may be noted. Equipment performance monitoring andtrending, and fault detection and diagnosis may be conducted, and reportand visualization may be generated regularly.

Big data analytics may be performed to gain insights from a big amountof building data in the cloud, which can help improve individualbuilding models.

A model update may be initiated from a cloud side and pushed to the edgedevices. One advantage is that one common model structure update may bepushed to all the edge devices simultaneously.

Details of a solution may be noted. On the edge side, the solutioncomprises of connecting the sensor signals to controllers and thensending to the optimization server and building management system like acompany enterprise buildings integrator via field bus, such as BACnet™,Modbus™, and so on.

The optimization server may contain an optimization engine whichperforms energy optimization based on real time data, building model andtariff information, periodically, e.g. every 15 minutes, by minimizingthe cost while meeting the comfort requirement. The optimization method(model predictive control (MPC) based) used in U.S. Pat. No. 10,101,048may still apply here. Optimized setpoints may be sent to sitecontrollers via a building management system and reset.

The building model may be built from historical data using statisticalregression with an on-the-fly parameter estimation and that is updatedwith new data gathered. The building model may be generated locally inthe edge side, or built remotely in the cloud side and then downloadedto the edge device.

Data may be pushed to an interne of things (IoT) hub on the cloud atperiodic intervals via a cloud connector and then stored in the datastore in the cloud.

On the cloud side, data analytics engine may perform the batch dataanalytics by combining the ontology models and data from different sitesand may generate valuable insights including performance monitoring andtrending, fault detection and diagnosis, improvement recommendation,which can be pushed to and visualized on customer portal and mobile appvia API.

An ontology model may be a meta-data structure which describes theproperties of equipment, a facility and building and theirrelationships. It may help a data analytics engine to understand thebuilding in details and generate insights, accordingly.

By leveraging a large amount of building data in cloud, the dataanalytics engine can implement big data analytics and improve individualbuilding models. A model update may be initiated from cloud side andpushed to the edge devices.

In the present way, the optimizer on the edge side will not necessarilyhave data transfer and control latency issue, while the cloud side mayimprove the model by leveraging big data analytics and push the updatedmodel to the edge side. The system may be an HVAC supervisory controlsolution with properties as noted herein.

The present system may reduce data transfer and control latency. It maydistribute the computational effort from the cloud to the edge.

Insights and/or models may be derived from a big amount of building datain the cloud which can be used to improve individual building models.

Distribution of computational load may be important for deployment of alarge number (>100 k) of buildings.

The present system may enable significant energy cost savings (gas,electricity, chilled water, and so forth) by 10 to 40 percent whencompared with the state-of-the-art.

The present system may enable deployment of the solution to customersthat are sensitive to send sensor data to cloud (e.g., banks).

The system may can be developed into one independent energy offering, orintegrated into a building management system as one powerful energyrelated feature.

The present system may improve building management system offering insupervisory closed loop control.

The system may have cloud plus edge computing. There may be control andoptimization on the edge while monitoring and tuning on cloud. A modelupdate may be initiated from cloud side and pushed to the edge devices.There may be setpoint optimization on the edge device.

There may be analytics and performance monitoring and trending, faultdetection and diagnosis, improvement recommendation.

The system may have a software component. The stack level may involve acloud, a secure, scalable infrastructure for collecting, aggregating andstoring data, allowing connected “things” to communicate, offering aSaaS solution as available, and IaaS/PaaS, data lakes.

A software type may involve connected/connectivity with an offeringavailable thru cloud or direct, remote connection (SaaS) or coverinfrastructure enabling connected services (sentience).

There may be an IoT stack Level of a cloud that is a secure, scalableinfrastructure for collecting, aggregating and storing data, allowingconnected “things” to communicate, offering a SaaS solution, and asavailable, IaaS/PaaS, and data lakes.

The system may generate or capture data. The type of data may beequipment sensor data. The data may reside in the edge device and thecloud.

FIG. 1 is a diagram of an architecture 10 for the present system. Thereis a cloud side 11 and an edge side 12. A cloud connector 13 may provideone or more paths between edge side 12 and cloud side 11. A cloud 14, onthe cloud side 11, may receive building management system data frombuilding management system 15 along connection 17 on the edge side 12.Building management system 15 may have a two way connection 16 with acontroller 18, which has a two way connection 19 with equipment 21. Anenergy optimizer 22 may have a two way connection 23 with cloud 14 formodel update information. Energy optimizer 22 may have a two wayconnection 24 with building management system 15 for set point resetinformation. Cloud 14 may have a connection 25 to a display mechanism 26such as a pad, laptop, cell phone, computer display of one kind oranother, a customer portal, and so on. Cloud 14 may perform analytics oninformation provided to it and provide reports. Cloud 14 may providedata analytics, performance monitoring, improvement recommendations andmodel update information.

FIG. 2 is a diagram of a solution architecture 30. Major components ofarchitecture 30 may incorporate cloud 31, optimizer 32, buildingmanagement system module 33, and interface 34. Cloud 31 may receive datato its data store 35 from an IoT hub 36. Data from data store 35 andinformation from an ontology model 37 may go to a data analytics engine38. Results from data analytics engine 38 may go to an insight module39. Insight information may go from module 39 to IoT hub 36 and to anapplication programmable interface (API) 41 of interface 34. An outputfrom API may go to a customer portal 42, a mobile app 43, and otheroutputs, of interface 34.

Coming in and going from a cloud connector 44 may be data or informationto or from optimizer 32. Optimizer 32 may have a building model module45. A data store 46 may have an output to module 45 and an output to anoptimizer engine 47. Data store 46 may have an input from a field bus 48which may have a two-way connection with a controller 49, which maymonitor or control information or data from one or more field sensors51, to or from field bus 48. Field sensors 51 may be placed so as toobtain data of a building, facility, enclosed environment, and/or thelike, relating to temperature, humidity, noise, fumes, physicaldisturbance, and/or other detectable parameters.

A building management system 52 of building management system module 33may have a two-way connection with field bus 48 and another two-wayconnection with optimization engine 47 of optimizer 32. Another cloudconnector 53 may have an input from building management system 52 ofmodule 33 and an output connected to IoT hub 36.

FIG. 3 is a diagram of a work flow of the present system blocknomenclature that may be used for illustrative purposes. From a start61, one may go to block 62 to collect data from field sensors. The datafrom block 62 may go one or more of block 63 and block 64. At block 63,a building model may be retrieved from a block 64 and tariff informationmay be retrieved from a block 65. Optimized set points from block 63 maybe sent by block 66 to a field controller and thus this flow may beended as indicated by block 67.

From block 68 where data is sent to a cloud via IoT hub, a batch dataanalytics is performed by combining ontology models and data fromdifferent sites as indicated by a retrieval of ontology models at block71 and retrieval of history data at block 72, according to block 69.Results of the analytics may go from block 69 to one or more of block 73and block 74. Block 73 indicates generating valuable insights which maygo to a block 75. Insights may be sent to a customer portal/mobile appfor visualization and notification according to block 75. Then thisprocess may end as indicated by block 76.

Block 74, which follows block 69, may improve individual buildingmodels. An updated module may go to an edge model store, in view ofblock 77. Then this process may end as indicated by block 78.

FIG. 4 is a diagram 81 of an example implementation of edgeoptimization. More particularly, the diagram is a sample of an edgeoptimization implementation in C# (C Sharp) language.

FIG. 5 is a diagram 82 of an example implementation of cloud analytics.

To recap, an integrated energy optimizer may incorporate an edge sideand a cloud side. The edge side may have an energy optimizer, a buildingmanagement system connected to the energy optimizer, a controllerconnected to the building management system, and equipment connected tothe controller. The cloud side may have a cloud connected to the energyoptimizer and to the building management system, and a user interfaceconnected to the cloud. The optimizer may be configured to meet anenergy demand with minimal cost based on current measurements ofbuilding parameters.

The energy optimizer may be connected to the cloud via a cloudconnector. The building management system may be connected to the cloudvia the cloud connector.

An optimization approach may incorporate collecting environmental datafrom field sensors at one or more sites, retrieving ontology models,retrieving historical data, performing batch data analytics on theontology models and environmental data from the field sensors at the oneor more sites, generating insights from the batch data analytics,sending insights to a customer interface for visualization andnotification, and optimizing energy based on the environmental data,building models and cost information.

The field sensors may be placed at the one or more sites in a fashion soas to obtain environmental data of a building, facility, or an enclosedenvironment, relating to temperature, humidity, noise, fumes, physicaldisturbance, and/or other detectable parameters.

One or more building models may be built from the historical data usingstatistical regression with an on-the-fly environmental data estimationthat is updated with environmental data collected.

Optimizing energy may be based on real time environmental data, buildingmodels and cost information, periodically, by minimizing the cost whilemeeting a predetermined comfort requirement for the one or more sites.

Periodically may mean a repetitive time period between one minute andsixty minutes.

The optimization approach may further incorporate sending theenvironmental data via an edge device and an IoT hub to a cloud.

The one or more building models may be generated locally in the edgeside, or built remotely in the cloud side and then downloaded to an edgedevice.

At the cloud, a data analytics engine may perform the batch dataanalytics by combining one or more of the ontology models andenvironmental data from one or more sites and generate one or moreinsights from a group of items having performance monitoring andtrending, fault detection and diagnosis, and improvementrecommendations.

The one or more of the ontology models may be meta-data structures whichdescribe properties of equipment in a building and their relationships.

An ontology model may help a data analytics engine to understand thebuilding in details and generate insights, accordingly.

The optimizing energy may be based on model predictive control (MPC).

The optimizing energy may lead to optimal setpoints for controllers atthe one or more sites.

An IoT hub energy optimizer may incorporate an optimizer connected to anIoT hub and a field bus, a building management system connected to theIoT hub and the field bus, a cloud connected to the IoT hub, and a userinterface connected to the cloud. The field bus may be connected to atleast one field sensor.

Data from the field sensor may go to the optimizer and the buildingmanagement system. The data may be processed at the optimizer and thebuilding management system for proper settings at the buildingmanagement system.

The cloud may incorporate a data store connected to the IoT hub, aninsight module connected to the IoT hub, an ontology model module, and adata analytics engine connected to the data store, the ontology modelmodule, and the insight module. The insight module may provide one ormore items selected from a group that incorporates performancemonitoring, trending, fault detection, diagnosis, and improvementrecommendations.

The user interface may be connected to the insight module.

The user interface may incorporate an application programmable interface(API) connected to the insight module, and a customer portal connectedto the API.

The user interface may further incorporate a mobile app.

U.S. patent application Ser. No. 13/860,318, filed Apr. 10, 2013, andentitled “Supervisory Controllers for HVAC Systems”, now U.S Pat. No.10,101,048, issued Oct. 16, 2018, may be related to the present system.U.S. patent application Ser. No. 13/860,318, filed Apr. 10, 2013, ishereby incorporated by reference.

Any publication or patent document noted herein is hereby incorporatedby reference to the same extent as if each publication or patentdocument was specifically and individually indicated to be incorporatedby reference.

In the present specification, some of the matter may be of ahypothetical or prophetic nature although stated in another manner ortense.

Although the present system and/or approach has been described withrespect to at least one illustrative example, many variations andmodifications will become apparent to those skilled in the art uponreading the specification. It is therefore the intention that theappended claims be interpreted as broadly as possible in view of therelated art to include all such variations and modifications.

What is claimed is:
 1. An integrated energy optimizer comprising: anedge side; and a cloud side; and wherein: the edge side comprises: anenergy optimizer; a building management system connected to the energyoptimizer; a controller connected to the building management system; andequipment connected to the controller; and the cloud side comprises: acloud connected to the energy optimizer and to the building managementsystem; and a user interface connected to the cloud; and wherein theoptimizer is configured to meet an energy demand with minimal cost basedon current measurements of building parameters.
 2. The optimizer ofclaim 1, wherein: the energy optimizer is connected to the cloud via acloud connector; and the building management system is connected to thecloud via the cloud connector.